# Microsoft launches MAI-Transcribe-1 multilingual speech recognition > MAI-Transcribe-1 entered public preview with support for 25 languages, giving teams another speech model to evaluate against real recordings. Canonical URL: https://www.devobs.io/news/news-mai-transcribe-1-launch/ By: Elias Brooks Published: 2026-09-06T11:58:54.629Z Updated: 2026-09-06T11:58:54.629Z Event date: 2026-04-02 Section: AI Microsoft announced MAI-Transcribe-1 on April 2 as a multilingual speech-to-text model covering 25 languages. The [launch post](https://microsoft.ai/news/state-of-the-art-speech-recognition-with-mai-transcribe-1/) placed it in public preview through Microsoft Foundry and the MAI Playground, with phased integration into Copilot Voice and Teams. ## A new transcription option Microsoft emphasized recordings with background noise, different accents and overlapping speech. It also published benchmark comparisons. Those results are the vendor’s evaluation, rather than measurements performed by this publication, and should not replace testing on a prospective user’s own audio. The relevant product change is straightforward: teams gained another model they could evaluate for turning recordings into text. ## Score the expensive mistakes separately For our recommended evaluation, aggregate word error rate should sit beside task-specific checks. A transcript can look broadly readable while misspelling a customer name, changing a number or dropping a negation. If the next step creates a support ticket or summarizes a decision, those particular errors deserve their own acceptance criteria. Assemble recordings that reflect the intended environment, then annotate the entities and statements that must survive transcription. Compare models on the same inputs and keep the unedited transcript available for inspection. Include interruptions and low-quality microphones deliberately rather than relying only on clean sample clips. ## Define the downstream contract Decide whether an application needs a batch transcript, live partial text or speaker attribution before treating any preview as a complete replacement for an existing service. These are separate product requirements. A useful rollout would also specify how a person corrects a transcript and how those corrections reach downstream summaries or search indexes. That workflow gives the model’s output a clear operational role instead of silently treating every recognized word as authoritative. ## Source references -